Análisis de Enlaces Web
Carlos Ernesto Martinez
2023-08-12
Presentación
A partir del análisis realizado por la Dra.
Primer Análisis
Sección 1 - Página Principal
Data Analysis module allows the user to have a preliminary analysis for quality control based on the data distribution per sample, using plots below
Fig. 1. CPM, Counts Per
Million Plot
The Count per million (CPM) plot shows the number of genes within each sample having more than 0 CPM, or more than 1, 2, 5 and 10 CPM. This plot could help the user decide the threshold to remove genes that appear to be very lowly expressed in any of the experimental conditions.
Fig. 2. Boxplot with pseudo
counts
Samples boxplot provides an easy way to visualize the distribution of pseudocounts in each sample, since it shows statistical measures such as median, quartiles, minimum and maximum. Whiskers are also drawn extending beyond each end of the box with points beyond the whiskers typically indicating outliers.
Fig. 3. Density plot
Pseudocounts distributions can also be summarized by means of a density plot. Density plot provides more detail by enabling, for example, the detection of dissimilarity in replicates’ behavior.
Fig. 4.
Multi-dimensional scaling plot
Multidimensional Scaling (MDS) is a technique that is used to create a visual representation of the pattern of proximities (similarities, dissimilarities, or distances) among a set of objects. In the context of RNA-Seq analysis, MDS plot shows variation among RNA-seq samples, distance between sample labels indicates dissimilarity.
Fig. 5. Principal component
plot
This type of plot is useful for visualizing the overall effect of experimental covariates and batch effects. In the context of RNA-Seq analysis, PCA essentially clusters samples by groups of the most significantly deregulated genes. Clustering first by the most significant group, then by progressively less significant groups.
Fig. 6. Boxplot
of standardized counts with TMM
Fig. 7. Multi-dimensional scaling plot of standardized counts with
TMM
Fig. 8. Principal component plot of standardized counts with
TMM
This type of graph is useful for visualizing the overall effect of experimental covariances and their batch effects. When a pair of samples, under the same condition, tend to group together, a lot effect can be suspected.
From this point, the same graphs are replicated, but they show the raw counts normalized with the TMM method (Trimmed Mean of M-values), (1) Robinson.
Sección 2 - EdgeR.php
In this section, we present the results of the differential expression analysis generated by the edgeR method. (1)
Result Files
The following text files contain the results of differential expression analysis for various comparisons:
The text files can be opened in Excel and contain the result of the differential expression analysis for all genes with significant counts. The meaning of each column is as follows:
- id: The gene ID (gene, transcript, etc.)
- log2FoldChange: The log fold-change between conditions being tested
- logCPM: Average log2-counts per million, averaged over all libraries
- p value: The statistical significance of the change
- FDR: False Discovery Rate adjusted for multiple testing with the Benjamini-Hochberg procedure
The last columns correspond to:
- raw counts: Raw counts for all samples
- normalized counts: Normalized counts for all samples
- Regulation: Gene regulation, indicating how and in which condition the gene was expressed
pEhExvsU2AF84_pval.txtpEhExvsEhMyb10.txtpEhExvsUmasM.txtpEhExvsCDC5.txtpEhExvsEhMyb10_Abundances.txtpEhExvsU2AF84_TOP.txtpEhExvsUmasM_logFC.txtpEhExvsCmasM.txtpEhExvsCmasM_Abundances.txtpEhExvsUmasM_TOP.txtpEhExvsUmasM_pval.txtpEhExvsU2AF84_Abundances.txtpEhExvsU2AF84_logFC.txtpEhExvsCDC5_logFC.txtpEhExvsCmasM_logFC.txtpEhExvsCDC5_Abundances.txtpEhExvsUmasM_Abundances.txtpEhExvsEhMyb10_TOP.txtpEhExvsCDC5_pval.txtpEhExvsEhMyb10_pval.txtpEhExvsCmasM_TOP.txtpEhExvsEhMyb10_logFC.txtpEhExvsCDC5_TOP.txtpEhExvsCmasM_pval.txtpEhExvsU2AF84.txt
Subgroup Analysis
Additional analysis files:
These “TOP.txt” files represent subgroup analyses and contain results only for differentially expressed genes based on the established cutoff parameters.
pEhExvsU2AF84_TOP.txtpEhExvsUmasM_TOP.txtpEhExvsEhMyb10_TOP.txtpEhExvsCmasM_TOP.txtpEhExvsCDC5_TOP.txt
The following files have genes that it was reported as differential expressed for more than one method,(EdgeR + other)
Gene Expression Plots
In the context of RNA-Seq analysis, MDS plot shows variation among RNA-seq samples, with the distance between sample labels indicating dissimilarity. When the experiment is well controlled, the greatest sources of variation should correspond to the treatments/groups of interest.
The Smear plot visualizes the results of a DE analysis, showing the log-fold change against log-counts per million. Differentially expressed features are highlighted in red.
The Volcano plot summarizes both fold-change and p-values. It is a scatter-plot of the negative log10-transformed p-values against the log2 fold change. Features declared as differentially expressed are highlighted in red.
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Sección 3 - DESeq2.php
DESeq2 Results
Diferential Expression by DESeq2. (1)
The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of e ach column is described below.
- id : The first column is the id(gene, transcript, etc.)
- baseMean: Mean normalised counts, averaged over all samples from the two conditions
- log2FoldChange : logaritmo de la magnitud de cambio entre el par de condiciones, en la tercer columna
- lfcSE: Standard errors of logarithm fold change
- Stat: test statistics
- pvalue: p value for the statistical significance of this change
- padj: p value adjusted for multiple testing with the Benjamini-Hochberg procedure The last columns correspond to:
- raw counts: Raw counts for all sample
- normalized counts: Normalized counts for all samples
- Regulation : gene regulation, indicating how and in which condition the genes was expressed
The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters).
The following files have genes that it was reported as differential expressed for more than one method (DESeq2 + other)
Graficas
plotPCA.pdfThis type of plot is useful for visualizing the overall effect of experimental covariates and batch effects (Love MI 2014).
plotMA.pdf, This plot represents each gene with a dot. The x axis is the average expression over the mean of normalized counts, the y axis is the log2 fold change between conditions. Features declared as differentially expressed are highlighted in red (Gonzalez 2014). References:
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Sección 4 - limma.php
limma Results
In this section the differential expression results generated by the limma method are shown.(1)
The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of each column is described below. :
id : The first column is the id(gene, transcript, etc.), log2FoldChange : The log fold-change between conditions being tested AveExpr: Average log2 expression t: the t-statistic used to assess differential expression P.Value: The p-value for differential expression; this value is not adjusted for multiple testing adj.P.Val :The p-value adjusted for multiple testing B: The B-statistic is the log-odds that the gene is differentially expressed The last columns correspond to: raw counts: Raw counts for all sample normalized counts: Normalized counts for all samples Regulation : gene regulation, indicating how and in which condition the genes was expressed
The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters). The following files have genes that it was reported as differential expressed for more than one method,(limma + other)
Graph PlotPCA
the graph plotPCA.pdf is useful for visualizing the overall effect of experimental covariates and batch effects (Love MI 2014).
In plot plotMA.pdf it represents each gene with a dot. The x axis is the average expression over the mean of normalized counts, the y axis is the log2 fold change between conditions. Features declared as differentially expressed are highlighted in red (Gonzalez 2014).
Click to enlarge the image
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Sección 5 - NOISeq.php
NOISeq Results
In this section the differential expression results generated by the NOISeq method are shown. (1)
The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of each column is described below. :
- id : The first column is the id(gene, transcript, etc.)
- Cond1_mean: The second column is the mean of the biological replicates of first condition
- Cond2_mean: the thirds column is the mean of the biological replicates of secondth condition
- theta: the fourth column is a Differential expression statistics
- Prop: the fifth column show Probability of differential expression
- log2FoldChange : The log fold-change between conditions being tested The last columns correspond to:
- raw counts: Raw counts for all sample
- normalized counts: Normalized counts for all samples
- DE: gene regulation, indicating how and in which condition the genes was expressed
Subgrupos
The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters).
Differential expressed
The following files have genes that it was reported as differential expressed for more than one method,(NOISeq + others)
Sección 6 - VennDiagram.php
Results Integration
Once the results of the differential expression analyses has been obtained (by the different selected methods), these results are compared, by mean of Venn, bar and correlograms plots, in order to see how the different methods agree on the final list of differential expressed genes
In the process of integration of results, different files are obtained. These files contain information related to the differentially expressed genes found at the intersection of all methods. The description of the files is as follows:
Files with _table.txt ending: File of binary values, which indicate for each gene, the methods that report it as differentially expressed. In the last column of the file, a description of the gene regulation can be found, where is indicated how and in which condition the genes was expressed.
Files with _IntersectSummary.txt ending: Contains the summary of the number of DE genes in all possible logical relationships between the different methods
Files with _Intesrsect_TOP_IDs.txt ending: Table that contains the ID of those genes that are at the intersection of the DE genes obtained by all the different selected methods.
Files with _logFCTable.txt ending: Table containing the logFC values of the DE genes reported for all methods
Files with _AbundanceTable.txt ending: Table containing the raw and normalized counts of all samples of the DE genes reported for all methods
Files with _PvalTable.txt ending: Table containing the padjust/FDR values of the DE genes reported for all methods
The Venn diagram shows the intersection of the differentially expressed genes reported by each of the selected methods. Further analysis could be considered only in the list of genes that were identified as differentials expressed by more than one method. (In the center of the graph)
Diagramas de Venn
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Graficos
Files
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upSetR files
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UnionHeathMap files
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pvalCorrelation files
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logFCCorrelation files
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AbundanceCorrelation files
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IntersectHeatmap Files
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Segundo Análisis
Sección 2.1 - Página Principal
Data Analysis module allows the user to have a preliminary analysis for quality control based on the data distribution per sample, using plots below
Fig. 1. CPM, Counts Per
Million Plot
The Count per million (CPM) plot shows the number of genes within each sample having more than 0 CPM, or more than 1, 2, 5 and 10 CPM. This plot could help the user decide the threshold to remove genes that appear to be very lowly expressed in any of the experimental conditions.
Fig. 2. Boxplot with pseudo
counts
Samples boxplot provides an easy way to visualize the distribution of pseudocounts in each sample, since it shows statistical measures such as median, quartiles, minimum and maximum. Whiskers are also drawn extending beyond each end of the box with points beyond the whiskers typically indicating outliers.
Fig. 3. Density plot
Pseudocounts distributions can also be summarized by means of a density plot. Density plot provides more detail by enabling, for example, the detection of dissimilarity in replicates’ behavior.
Fig. 4.
Multi-dimensional scaling plot
Multidimensional Scaling (MDS) is a technique that is used to create a visual representation of the pattern of proximities (similarities, dissimilarities, or distances) among a set of objects. In the context of RNA-Seq analysis, MDS plot shows variation among RNA-seq samples, distance between sample labels indicates dissimilarity.
Fig. 5. Principal component
plot
This type of plot is useful for visualizing the overall effect of experimental covariates and batch effects. In the context of RNA-Seq analysis, PCA essentially clusters samples by groups of the most significantly deregulated genes. Clustering first by the most significant group, then by progressively less significant groups.
Fig. 6. Boxplot
of standardized counts with TMM
Fig. 7. Multi-dimensional scaling plot of standardized counts with
TMM
Fig. 8. Principal component plot of standardized counts with
TMM
This type of graph is useful for visualizing the overall effect of experimental covariances and their batch effects. When a pair of samples, under the same condition, tend to group together, a lot effect can be suspected.
From this point, the same graphs are replicated, but they show the raw counts normalized with the TMM method (Trimmed Mean of M-values), (1) Robinson.
Sección 2.2 - EdgeR.php
In this section, we present the results of the differential expression analysis generated by the edgeR method. (1)
Gene Expression Plots
In the context of RNA-Seq analysis, MDS plot shows variation among RNA-seq samples, with the distance between sample labels indicating dissimilarity. When the experiment is well controlled, the greatest sources of variation should correspond to the treatments/groups of interest.
The Smear plot visualizes the results of a DE analysis, showing the log-fold change against log-counts per million. Differentially expressed features are highlighted in red.
The Volcano plot summarizes both fold-change and p-values. It is a scatter-plot of the negative log10-transformed p-values against the log2 fold change. Features declared as differentially expressed are highlighted in red.
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Sección 3 - DESeq2.php
DESeq2 Results
Diferential Expression by DESeq2. (1)
The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of e ach column is described below.
- id : The first column is the id(gene, transcript, etc.)
- baseMean: Mean normalised counts, averaged over all samples from the two conditions
- log2FoldChange : logaritmo de la magnitud de cambio entre el par de condiciones, en la tercer columna
- lfcSE: Standard errors of logarithm fold change
- Stat: test statistics
- pvalue: p value for the statistical significance of this change
- padj: p value adjusted for multiple testing with the Benjamini-Hochberg procedure The last columns correspond to:
- raw counts: Raw counts for all sample
- normalized counts: Normalized counts for all samples
- Regulation : gene regulation, indicating how and in which condition the genes was expressed
pval files
pEhExvsCDC5_pval.txtpEhExvsCmasM_pval.txtpEhExvsEhMyb10_pval.txtpEhExvsU2AF84_pval.txtpEhExvsUmasM_pval.txt
Abundance files
logFC files
Other files
The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters).
The following files have genes that it was reported as differential expressed for more than one method (DESeq2 + other)
Graficas
plotPCA.pdfThis type of plot is useful for visualizing the overall effect of experimental covariates and batch effects (Love MI 2014).
plotMA.pdf, This plot represents each gene with a dot. The x axis is the average expression over the mean of normalized counts, the y axis is the log2 fold change between conditions. Features declared as differentially expressed are highlighted in red (Gonzalez 2014). References:
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Sección 4 - limma.php
limma Results
In this section the differential expression results generated by the limma method are shown.(1)
The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of each column is described below. :
id : The first column is the id(gene, transcript, etc.), log2FoldChange : The log fold-change between conditions being tested AveExpr: Average log2 expression t: the t-statistic used to assess differential expression P.Value: The p-value for differential expression; this value is not adjusted for multiple testing adj.P.Val :The p-value adjusted for multiple testing B: The B-statistic is the log-odds that the gene is differentially expressed The last columns correspond to: raw counts: Raw counts for all sample normalized counts: Normalized counts for all samples Regulation : gene regulation, indicating how and in which condition the genes was expressed
Main Files
pval files
logFC files
Abundance files
The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters). The following files have genes that it was reported as differential expressed for more than one method,(limma + other)
Graph PlotPCA
the graph plotPCA.pdf is useful for visualizing the overall effect of experimental covariates and batch effects (Love MI 2014).
In plot plotMA.pdf it represents each gene with a dot. The x axis is the average expression over the mean of normalized counts, the y axis is the log2 fold change between conditions. Features declared as differentially expressed are highlighted in red (Gonzalez 2014).
Click to enlarge the image
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Sección 5 - NOISeq.php
NOISeq Results
In this section the differential expression results generated by the NOISeq method are shown. (1)
The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of each column is described below. :
- id : The first column is the id(gene, transcript, etc.)
- Cond1_mean: The second column is the mean of the biological replicates of first condition
- Cond2_mean: the thirds column is the mean of the biological replicates of secondth condition
- theta: the fourth column is a Differential expression statistics
- Prop: the fifth column show Probability of differential expression
- log2FoldChange : The log fold-change between conditions being tested The last columns correspond to:
- raw counts: Raw counts for all sample
- normalized counts: Normalized counts for all samples
- DE: gene regulation, indicating how and in which condition the genes was expressed
Main Files
pval files
logFC files
Abundance files
The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters). The following files have genes that it was reported as differential expressed for more than one method,(limma + other)
Differential expressed
The following files have genes that it was reported as differential expressed for more than one method,(NOISeq + others)
Graph PlotPCA
the graph plotPCA.pdf is useful for visualizing the overall effect of experimental covariates and batch effects (Love MI 2014).
In plot plotMA.pdf it represents each gene with a dot. The x axis is the average expression over the mean of normalized counts, the y axis is the log2 fold change between conditions. Features declared as differentially expressed are highlighted in red (Gonzalez 2014).
Click to enlarge the image
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Sección 6 - VennDiagram.php
Results Integration
Once the results of the differential expression analyses has been obtained (by the different selected methods), these results are compared, by mean of Venn, bar and correlograms plots, in order to see how the different methods agree on the final list of differential expressed genes
In the process of integration of results, different files are obtained. These files contain information related to the differentially expressed genes found at the intersection of all methods. The description of the files is as follows:
Files with _table.txt ending: File of binary values, which indicate for each gene, the methods that report it as differentially expressed. In the last column of the file, a description of the gene regulation can be found, where is indicated how and in which condition the genes was expressed.
Files with _IntersectSummary.txt ending: Contains the summary of the number of DE genes in all possible logical relationships between the different methods
Files with _Intesrsect_TOP_IDs.txt ending: Table that contains the ID of those genes that are at the intersection of the DE genes obtained by all the different selected methods.
Files with _logFCTable.txt ending: Table containing the logFC values of the DE genes reported for all methods
Files with _AbundanceTable.txt ending: Table containing the raw and normalized counts of all samples of the DE genes reported for all methods
Files with _PvalTable.txt ending: Table containing the padjust/FDR values of the DE genes reported for all methods
MatrizWeight files
IntersectSummary files
pEhExvsCDC5_IntersectSummary.txtpEhExvsCmasM_IntersectSummary.txtpEhExvsEhMyb10_IntersectSummary.txtpEhExvsU2AF84_IntersectSummary.txt
Table files
AbundanceTable files
pEhExvsCDC5_AbundanceTable.txtpEhExvsCmasM_AbundanceTable.txtpEhExvsEhMyb10_AbundanceTable.txtpEhExvsU2AF84_AbundanceTable.txt
logFC_Table files
pEhExvsCDC5_logFCTable.txtpEhExvsCmasM_logFCTable.txtpEhExvsEhMyb10_logFCTable.txtpEhExvsU2AF84_logFCTable.txt
PvalTable files
pEhExvsCDC5_PvalTable.txtpEhExvsCmasM_PvalTable.txtpEhExvsEhMyb10_PvalTable.txtpEhExvsU2AF84_PvalTable.txt
TOPs files
pEhExvsCDC5_Intesrsect_TOP_IDs.txtpEhExvsCDC5_Union_TOP_IDs.txtpEhExvsCmasM_Intesrsect_TOP_IDs.txtpEhExvsCmasM_Union_TOP_IDs.txtpEhExvsEhMyb10_Intesrsect_TOP_IDs.txtpEhExvsEhMyb10_Union_TOP_IDs.txtpEhExvsU2AF84_Intesrsect_TOP_IDs.txtpEhExvsU2AF84_Union_TOP_IDs.txt
The Venn diagram shows the intersection of the differentially expressed genes reported by each of the selected methods. Further analysis could be considered only in the list of genes that were identified as differentials expressed by more than one method. (In the center of the graph)
Diagramas de Venn
Graficos
Files
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PvalCorrelation files
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UpsetR files
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IntersectionHeathMap Files
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UnionHeathMap Files
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logFCCCorrelation files
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AbundanceCorrelation files
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Tercer Análisis
Sección 1 - Página Principal
Data Analysis module allows the user to have a preliminary analysis for quality control based on the data distribution per sample, using plots below
Fig. 1. CPM, Counts Per
Million Plot
The Count per million (CPM) plot shows the number of genes within each sample having more than 0 CPM, or more than 1, 2, 5 and 10 CPM. This plot could help the user decide the threshold to remove genes that appear to be very lowly expressed in any of the experimental conditions.
Fig. 2. Boxplot with pseudo
counts
Samples boxplot provides an easy way to visualize the distribution of pseudocounts in each sample, since it shows statistical measures such as median, quartiles, minimum and maximum. Whiskers are also drawn extending beyond each end of the box with points beyond the whiskers typically indicating outliers.
Fig. 3. Density plot
Pseudocounts distributions can also be summarized by means of a density plot. Density plot provides more detail by enabling, for example, the detection of dissimilarity in replicates’ behavior.
Fig. 4.
Multi-dimensional scaling plot
Multidimensional Scaling (MDS) is a technique that is used to create a visual representation of the pattern of proximities (similarities, dissimilarities, or distances) among a set of objects. In the context of RNA-Seq analysis, MDS plot shows variation among RNA-seq samples, distance between sample labels indicates dissimilarity.
Fig. 5. Principal component
plot
This type of plot is useful for visualizing the overall effect of experimental covariates and batch effects. In the context of RNA-Seq analysis, PCA essentially clusters samples by groups of the most significantly deregulated genes. Clustering first by the most significant group, then by progressively less significant groups.
Fig. 6. Boxplot
of standardized counts with TMM
Fig. 7. Multi-dimensional scaling plot of standardized counts with
TMM
Fig. 8. Principal component plot of standardized counts with
TMM
This type of graph is useful for visualizing the overall effect of experimental covariances and their batch effects. When a pair of samples, under the same condition, tend to group together, a lot effect can be suspected.
From this point, the same graphs are replicated, but they show the raw counts normalized with the TMM method (Trimmed Mean of M-values), (1) Robinson.
Sección 2 - DESeq2.php
In this section, we present the results of the differential expression analysis generated by the edgeR method. (1)
Result Files
The following text files contain the results of differential expression analysis for various comparisons:
The text files can be opened in Excel and contain the result of the differential expression analysis for all genes with significant counts. The meaning of each column is as follows:
- id: The gene ID (gene, transcript, etc.)
- log2FoldChange: The log fold-change between conditions being tested
- logCPM: Average log2-counts per million, averaged over all libraries
- p value: The statistical significance of the change
- FDR: False Discovery Rate adjusted for multiple testing with the Benjamini-Hochberg procedure
The last columns correspond to:
- raw counts: Raw counts for all samples
- normalized counts: Normalized counts for all samples
- Regulation: Gene regulation, indicating how and in which condition the gene was expressed
Files
pval files
pEhExvsCDC5_pval.txtpEhExvsCmasM_pval.txtpEhExvsEhMyb10_pval.txtpEhExvsU2AF84_pval.txtpEhExvsUmasM_pval.txt
logFC files
pEhExvsCDC5_logFC.txtpEhExvsCmasM_logFC.txtpEhExvsEhMyb10_logFC.txtpEhExvsU2AF84_logFC.txtpEhExvsUmasM_logFC.txt
Abundance files
Subgroup Analysis
Additional analysis files:
These “TOP.txt” files represent subgroup analyses and contain results only for differentially expressed genes based on the established cutoff parameters.
pEhExvsCDC5_TOP.txtpEhExvsCmasM_TOP.txtpEhExvsEhMyb10_TOP.txtpEhExvsU2AF84_TOP.txtpEhExvsUmasM_TOP.txt
The following files have genes that it was reported as differential expressed for more than one method,(EdgeR + other)
Intersect files
Gene Expression Plots
In the context of RNA-Seq analysis, MDS plot shows variation among RNA-seq samples, with the distance between sample labels indicating dissimilarity. When the experiment is well controlled, the greatest sources of variation should correspond to the treatments/groups of interest.
The Smear plot visualizes the results of a DE analysis, showing the log-fold change against log-counts per million. Differentially expressed features are highlighted in red.
The Volcano plot summarizes both fold-change and p-values. It is a scatter-plot of the negative log10-transformed p-values against the log2 fold change. Features declared as differentially expressed are highlighted in red.
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Sección 3 - Edge.php
edgeR Results
Diferential Expression by DESeq2. (1)
The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of e ach column is described below.
- id : The first column is the id(gene, transcript, etc.)
- baseMean: Mean normalised counts, averaged over all samples from the two conditions
- log2FoldChange : logaritmo de la magnitud de cambio entre el par de condiciones, en la tercer columna
- lfcSE: Standard errors of logarithm fold change
- Stat: test statistics
- pvalue: p value for the statistical significance of this change
- padj: p value adjusted for multiple testing with the Benjamini-Hochberg procedure The last columns correspond to:
- raw counts: Raw counts for all sample
- normalized counts: Normalized counts for all samples
- Regulation : gene regulation, indicating how and in which condition the genes was expressed
Files
pval flies
pEhExvsCDC5_pval.txtpEhExvsCmasM_pval.txtpEhExvsEhMyb10_pval.txtpEhExvsU2AF84_pval.txtpEhExvsUmasM_pval.txt
logFC files
pEhExvsCDC5_logFC.txtpEhExvsCmasM_logFC.txtpEhExvsEhMyb10_logFC.txtpEhExvsU2AF84_logFC.txtpEhExvsUmasM_logFC.txt
Abundances files
pEhExvsCDC5_Abundances.txtpEhExvsCmasM_Abundances.txtpEhExvsEhMyb10_Abundances.txtpEhExvsU2AF84_Abundances.txtpEhExvsUmasM_Abundances.txt
The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters).
pEhExvsUmasM_TOP.txtpEhExvsU2AF84_TOP.txtpEhExvsEhMyb10_TOP.txtpEhExvsCmasM_TOP.txtpEhExvsCDC5_TOP.txt
The following files have genes that it was reported as differential expressed for more than one method (DESeq2 + other)
Graficas
plotPCA.pdfThis type of plot is useful for visualizing the overall effect of experimental covariates and batch effects (Love MI 2014).
plotMA.pdf, This plot represents each gene with a dot. The x axis is the average expression over the mean of normalized counts, the y axis is the log2 fold change between conditions. Features declared as differentially expressed are highlighted in red (Gonzalez 2014). References:
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Sección 4 - limma.php
limma Results
In this section the differential expression results generated by the limma method are shown.(1)
The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of each column is described below. :
id : The first column is the id(gene, transcript, etc.), log2FoldChange : The log fold-change between conditions being tested AveExpr: Average log2 expression t: the t-statistic used to assess differential expression P.Value: The p-value for differential expression; this value is not adjusted for multiple testing adj.P.Val :The p-value adjusted for multiple testing B: The B-statistic is the log-odds that the gene is differentially expressed The last columns correspond to: raw counts: Raw counts for all sample normalized counts: Normalized counts for all samples Regulation : gene regulation, indicating how and in which condition the genes was expressed
Files
pval files
pEhExvsCDC5_pval.txtpEhExvsCmasM_pval.txtpEhExvsEhMyb10_pval.txtpEhExvsU2AF84_pval.txtpEhExvsUmasM_pval.txt
logFC files
pEhExvsCDC5_logFC.txtpEhExvsCmasM_logFC.txt<pEhExvsEhMyb10_logFC.txt/code>pEhExvsU2AF84_logFC.txtpEhExvsUmasM_logFC.txt
The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters). The following files have genes that it was reported as differential expressed for more than one method,(limma + other)
pEhExvsCDC5_TOP.txt<pEhExvsCmasM_TOP.txt/code><pEhExvsEhMyb10_TOP.txt/code>pEhExvsU2AF84_TOP.txtpEhExvsUmasM_TOP.txt
Abundance files
pEhExvsCDC5_Abundances.txtpEhExvsCmasM_Abundances.txtpEhExvsEhMyb10_Abundances.txtpEhExvsU2AF84_Abundances.txtpEhExvsUmasM_Abundances.txt
Intersect files
Graph PlotPCA
the graph plotPCA.pdf is useful for visualizing the overall effect of experimental covariates and batch effects (Love MI 2014).
In plot plotMA.pdf it represents each gene with a dot. The x axis is the average expression over the mean of normalized counts, the y axis is the log2 fold change between conditions. Features declared as differentially expressed are highlighted in red (Gonzalez 2014).
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Sección 5 - NOISeq.php
NOISeq Results
In this section the differential expression results generated by the NOISeq method are shown. (1)
The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of each column is described below. :
- id : The first column is the id(gene, transcript, etc.)
- Cond1_mean: The second column is the mean of the biological replicates of first condition
- Cond2_mean: the thirds column is the mean of the biological replicates of secondth condition
- theta: the fourth column is a Differential expression statistics
- Prop: the fifth column show Probability of differential expression
- log2FoldChange : The log fold-change between conditions being tested The last columns correspond to:
- raw counts: Raw counts for all sample
- normalized counts: Normalized counts for all samples
- DE: gene regulation, indicating how and in which condition the genes was expressed
pval files
pEhExvsCDC5_pval.txtpEhExvsCmasM_pval.txtpEhExvsEhMyb10_pval.txtpEhExvsU2AF84_pval.txtpEhExvsUmasM_pval.txt
logFC files
pEhExvsCDC5_logFC.txtpEhExvsCmasM_logFC.txtpEhExvsEhMyb10_logFC.txtpEhExvsU2AF84_logFC.txtpEhExvsUmasM_logFC.txt
Abundance files
Subgrupos
The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters).
Differential expressed
The following files have genes that it was reported as differential expressed for more than one method,(NOISeq + others)
Graphs
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Sección 6 - VennDiagram.php
Results Integration
Once the results of the differential expression analyses has been obtained (by the different selected methods), these results are compared, by mean of Venn, bar and correlograms plots, in order to see how the different methods agree on the final list of differential expressed genes
In the process of integration of results, different files are obtained. These files contain information related to the differentially expressed genes found at the intersection of all methods. The description of the files is as follows:
Files with _table.txt ending: File of binary values, which indicate for each gene, the methods that report it as differentially expressed. In the last column of the file, a description of the gene regulation can be found, where is indicated how and in which condition the genes was expressed.
Files with _IntersectSummary.txt ending: Contains the summary of the number of DE genes in all possible logical relationships between the different methods
Files with _Intesrsect_TOP_IDs.txt ending: Table that contains the ID of those genes that are at the intersection of the DE genes obtained by all the different selected methods.
Files with _logFCTable.txt ending: Table containing the logFC values of the DE genes reported for all methods
Files with _AbundanceTable.txt ending: Table containing the raw and normalized counts of all samples of the DE genes reported for all methods
Files with _PvalTable.txt ending: Table containing the padjust/FDR values of the DE genes reported for all methods
Files
pEhExvsCDC5_table.txtpEhExvsCmasM_table.txtpEhExvsEhMyb10_table.txtpEhExvsU2AF84_table.txtpEhExvsUmasM_table
MatrixWeight files
pEhExvsCDC5_matrixWeight.txtpEhExvsCmasM_matrixWeight.txtpEhExvsEhMyb10_matrixWeight.txtpEhExvsU2AF84_matrixWeight.txtpEhExvsUmasM_matrixWeight.txt
PvalTable files
pEhExvsCDC5_PvalTable.txtpEhExvsCmasM_PvalTable.txtpEhExvsEhMyb10_PvalTable.txtpEhExvsU2AF84_PvalTable.txtpEhExvsUmasM_PvalTable.txt
IntersectTopIDs files
pEhExvsCDC5_Intesrsect_TOP_IDs.txtpEhExvsCmasM_Intesrsect_TOP_IDs.txtpEhExvsEhMyb10_Intesrsect_TOP_IDs.txtpEhExvsU2AF84_Intesrsect_TOP_IDs.txtpEhExvsUmasM_Intesrsect_TOP_IDs.txt
UnionTop_IDs files
pEhExvsCDC5_Union_TOP_IDs.txtpEhExvsCmasM_Union_TOP_IDs.txtpEhExvsEhMyb10_Union_TOP_IDs.txtpEhExvsU2AF84_Union_TOP_IDs.txtpEhExvsUmasM_Union_TOP_IDs.txt
logFCTable files
pEhExvsCDC5_logFCTable.txtpEhExvsCmasM_logFCTable.txtpEhExvsEhMyb10_logFCTable.txtpEhExvsU2AF84_logFCTable.txtpEhExvsUmasM_logFCTable.txt
IntersectSummary files
pEhExvsCDC5_IntersectSummary.txtpEhExvsCmasM_IntersectSummary.txtpEhExvsEhMyb10_IntersectSummary.txtpEhExvsU2AF84_IntersectSummary.txtpEhExvsUmasM_IntersectSummary.txt
AbundanceTable files
Diagramas de Venn
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Graficos
The Venn diagram shows the intersection of the differentially expressed genes reported by each of the selected methods. Further analysis could be considered only in the list of genes that were identified as differentials expressed by more than one method. (In the center of the graph)
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Upset files
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IntersectHeathmap files
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UnionHeathMaps files
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pvalCorrelation files
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logFCCorrelation files
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AbundanceCorrelation files
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Presentacion de las Pre-Clasificaciones
Primera Clasificacion: pEhEx
## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 72.594848 40.99764 280.54541 169.92967 411.34855 466.86626
## 2 EHI_000140A 111.027415 145.24877 643.77044 342.73950 324.16054 37.85402
## 3 EHI_000240A 876.831894 752.01378 1122.88829 1140.54489 1837.65493 2071.45616
## 4 EHI_000250A 492.506226 510.71341 232.49229 315.37794 310.74700 642.81736
## 5 EHI_000260A 12.810856 107.76521 2.11999 105.84602 63.71431 49.07003
## 6 EHI_000280A 58.360564 42.16900 171.71923 53.28303 79.36344 60.28603
## 7 EHI_000290A 17.081141 26.94130 14.13327 18.00102 13.41354 79.21304
## 8 EHI_000300A 49.819994 70.28166 48.05312 129.60737 109.54391 30.84402
## 9 EHI_000410A 14.234284 19.91314 52.29310 27.36156 23.47369 63.79104
## 10 EHI_000430A 9.963999 25.76994 2.11999 25.20143 13.41354 10.51501## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 72.59485 40.99764 280.54541 169.92967 411.34855 466.86626
## 2 EHI_000140A 111.02742 145.24877 643.77044 342.73950 324.16054 37.85402
## 3 EHI_000240A 876.83189 752.01378 1122.88829 1140.54489 1837.65493 2071.45616
## 4 EHI_000250A 492.50623 510.71341 232.49229 315.37794 310.74700 642.81736
## 5 EHI_000260A 12.81086 107.76521 2.11999 105.84602 63.71431 49.07003
## 6 EHI_000280A 58.36056 42.16900 171.71923 53.28303 79.36344 60.28603Log-Normalización
## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 72.59485 40.99764 280.54541 169.92967 411.34855 466.86626
## 2 EHI_000140A 111.02742 145.24877 643.77044 342.73950 324.16054 37.85402
## 3 EHI_000240A 876.83189 752.01378 1122.88829 1140.54489 1837.65493 2071.45616
## 4 EHI_000250A 492.50623 510.71341 232.49229 315.37794 310.74700 642.81736
## 5 EHI_000260A 12.81086 107.76521 2.11999 105.84602 63.71431 49.07003
## 6 EHI_000280A 58.36056 42.16900 171.71923 53.28303 79.36344 60.28603
## log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1 7.417259 8.687721 8.869952 6.201533 5.392236
## 2 8.425172 8.345008 5.279992 6.807708 7.192281
## 3 10.156772 10.844435 11.017126 9.777801 9.556532
## 4 8.305505 8.284232 9.330508 8.946924 8.999192
## 5 6.739389 6.016013 5.645875 3.787731 6.765073
## 6 5.762429 6.328467 5.937486 5.891433 5.431924
## log2samplevsCDC53
## 1 8.137224
## 2 9.332642
## 3 10.134283
## 4 7.867231
## 5 1.641542
## 6 7.432285##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## select## Loading required package: survival## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 72.59485 40.99764 280.54541 169.92967 411.34855 466.86626
## 2 EHI_000140A 111.02742 145.24877 643.77044 342.73950 324.16054 37.85402
## 3 EHI_000240A 876.83189 752.01378 1122.88829 1140.54489 1837.65493 2071.45616
## 4 EHI_000250A 492.50623 510.71341 232.49229 315.37794 310.74700 642.81736
## 5 EHI_000260A 12.81086 107.76521 2.11999 105.84602 63.71431 49.07003
## 6 EHI_000280A 58.36056 42.16900 171.71923 53.28303 79.36344 60.28603
## log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1 7.417259 8.687721 8.869952 6.201533 5.392236
## 2 8.425172 8.345008 5.279992 6.807708 7.192281
## 3 10.156772 10.844435 11.017126 9.777801 9.556532
## 4 8.305505 8.284232 9.330508 8.946924 8.999192
## 5 6.739389 6.016013 5.645875 3.787731 6.765073
## 6 5.762429 6.328467 5.937486 5.891433 5.431924
## log2samplevsCDC53
## 1 8.137224
## 2 9.332642
## 3 10.134283
## 4 7.867231
## 5 1.641542
## 6 7.432285Muestra 1
## [1] 6.30237## [1] 2.868113## [1] 7.417259 8.425172 10.156772 8.305505 6.739389## GenId CDC5_1 CDC5_2 CDC5_3
## Length:4772 Min. : 0.00 Min. : 0.00 Min. : 0.0
## Class :character 1st Qu.: 17.08 1st Qu.: 17.57 1st Qu.: 16.3
## Mode :character Median : 45.55 Median : 49.20 Median : 44.5
## Mean : 1749.28 Mean : 1748.01 Mean : 1980.0
## 3rd Qu.: 196.79 3rd Qu.: 208.50 3rd Qu.: 177.4
## Max. :270953.87 Max. :270338.41 Max. :481876.0
## pEhEx_1 pEhEx_2 pEhEx_3 log2sample1
## Min. : 0.0 Min. : 0.00 Min. : 0.0 Min. : 0.000
## 1st Qu.: 18.0 1st Qu.: 15.65 1st Qu.: 15.4 1st Qu.: 4.248
## Median : 50.4 Median : 49.18 Median : 54.0 Median : 5.684
## Mean : 1395.0 Mean : 1717.64 Mean : 1909.2 Mean : 6.302
## 3rd Qu.: 208.1 3rd Qu.: 223.84 3rd Qu.: 242.0 3rd Qu.: 7.708
## Max. :207266.7 Max. :265749.05 Max. :707261.7 Max. :17.661
## log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 4.057 1st Qu.: 4.038 1st Qu.: 4.176 1st Qu.: 4.215
## Median : 5.649 Median : 5.781 Median : 5.541 Median : 5.650
## Mean : 6.237 Mean : 6.186 Mean : 6.244 Mean : 6.270
## 3rd Qu.: 7.813 3rd Qu.: 7.925 3rd Qu.: 7.628 3rd Qu.: 7.711
## Max. :18.020 Max. :19.432 Max. :18.048 Max. :18.044
## log2samplevsCDC53
## Min. : 0.000
## 1st Qu.: 4.109
## Median : 5.508
## Mean : 6.114
## 3rd Qu.: 7.479
## Max. :18.878
## [1] 6.30237## [1] 2.868113## [1] 0.3887185 0.7401387 1.3438805 0.6984156 0.1523717 -0.1882564
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -9.053474e-17 0.01447452
## sd 9.998952e-01 0.01023499
## Loglikelihood: -6770.675 AIC: 13545.35 BIC: 13558.29
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Cálculo de cuantiles
## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8713278 0.8126272
## 70 -0.9260903 0.9617987
## 75 -0.9875514 1.1301158
## 80 -1.0213487 1.3734820
## 85 -1.0966280 1.6988689
## 90 -1.1851905 2.1819106
## 95 -1.3565981 2.6079805
## 99 -1.9245847 3.1666752
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra 2
## [1] 6.237216## [1] 3.103083## [1] 8.687721 8.345008 10.844435 8.284232 6.016013
Log-normalizacion
## [1] 6.237216## [1] 3.103083## [1] 0.78969998 0.67925752 1.48472313 0.65967167 -0.07128491 0.02940674
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -1.260395e-16 0.01447452
## sd 9.998952e-01 0.01023499
## Loglikelihood: -6770.675 AIC: 13545.35 BIC: 13558.29
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
Calculo de Cuantiles
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8926336 0.8466541
## 70 -0.9421687 0.9875692
## 75 -0.9976178 1.1428700
## 80 -1.0605870 1.3752461
## 85 -1.1334411 1.6895575
## 90 -1.2198720 2.1202686
## 95 -1.4640882 2.5136403
## 99 -2.0100061 3.0865078
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra 3
## [1] 6.186357## [1] 3.171257## [1] 8.869952 5.279992 11.017126 9.330508 5.645875
## [1] 6.186357## [1] 3.171257## [1] 0.84622446 -0.28580629 1.52329778 0.99145245 -0.17043143 -0.07847701
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 2.945427e-17 0.01447452
## sd 9.998952e-01 0.01023499
## Loglikelihood: -6770.675 AIC: 13545.35 BIC: 13558.29
## Correlation matrix:
## mean sd
## mean 1.000000e+00 1.684231e-11
## sd 1.684231e-11 1.000000e+00
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Calculo de Cuantiles
## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8676459 0.8379706
## 70 -0.9660265 0.9701830
## 75 -1.0458574 1.1161493
## 80 -1.1427428 1.3265284
## 85 -1.2660057 1.6213606
## 90 -1.4356115 2.0178765
## 95 -1.7090932 2.4795401
## 99 -1.9507587 3.0829063Creación de histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra pEhExvsCDC51
## [1] 6.244372## [1] 2.880412## [1] 6.201533 6.807708 9.777801 8.946924 3.787731
## [1] 6.244372## [1] 2.880412## [1] -0.01487273 0.19557464 1.22670936 0.93825188 -0.85287855 -0.12253093Primer histograma
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 6.276949e-17 0.01447452
## sd 9.998952e-01 0.01023499
## Loglikelihood: -6770.675 AIC: 13545.35 BIC: 13558.29
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Calculo de Cuantiles
## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8528786 0.8216616
## 70 -0.9073590 0.9496778
## 75 -0.9073590 1.1007491
## 80 -0.9684970 1.3308952
## 85 -1.0381490 1.6867728
## 90 -1.1190762 2.1306178
## 95 -1.2156475 2.6928135
## 99 -2.1678747 3.3316192Histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra pEhExvsCDC52
## [1] 6.269726## [1] 2.952289## [1] 5.392236 7.192281 9.556532 8.999192 6.765073Primer Histograma
## [1] 6.269726## [1] 2.952289## [1] -0.2972235 0.3124880 1.1133080 0.9245256 0.1677843 -0.2837806Segundo Histograma
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -3.587454e-18 0.01447452
## sd 9.998952e-01 0.01023499
## Loglikelihood: -6770.675 AIC: 13545.35 BIC: 13558.29
## Correlation matrix:
## mean sd
## mean 1.000000e+00 1.684231e-11
## sd 1.684231e-11 1.000000e+00
Cálculo de cuantiles
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8380866 0.7932340
## 70 -0.8811551 0.9359679
## 75 -0.9283894 1.1117853
## 80 -1.0392501 1.3412158
## 85 -1.1058050 1.6629743
## 90 -1.1828753 2.0953140
## 95 -1.3871587 2.6382746
## 99 -2.1236833 3.2437321Histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra pEhExvsCDC53
## [1] 6.11433## [1] 2.904448## [1] 8.137224 9.332642 10.134283 7.867231 1.641542** Primer histograma**
## [1] 6.11433## [1] 2.904448## [1] 0.6964814 1.1080632 1.3840678 0.6035230 -1.5399785 0.4537713Segundo histograma
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -9.490135e-17 0.01447452
## sd 9.998952e-01 0.01023499
## Loglikelihood: -6770.675 AIC: 13545.35 BIC: 13558.29
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
Calculo de Cuantiles
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8305939 0.8018512
## 70 -0.8877013 0.9412276
## 75 -0.9522378 1.1212912
## 80 -1.0264291 1.3220406
## 85 -1.1638210 1.5939827
## 90 -1.2824356 2.0684839
## 95 -1.4385679 2.6057307
## 99 -1.8396440 3.5572717Histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Segunda Clasificacion: pEhExvsCmasM
## # A tibble: 10 × 7
## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EHI_000130A 45.3 108. 129. 180. 446. 516.
## 2 EHI_000140A 66.0 318. 257. 363. 352. 41.8
## 3 EHI_000240A 701. 1282. 877. 1209. 1993. 2290.
## 4 EHI_000250A 707. 430. 389. 334. 337. 711.
## 5 EHI_000260A 94.5 109. 35.2 112. 69.1 54.2
## 6 EHI_000280A 58.3 50.5 80.8 56.5 86.1 66.6
## 7 EHI_000290A 27.2 14.9 23.9 19.1 14.5 87.6
## 8 EHI_000300A 60.9 143. 111. 137. 119. 34.1
## 9 EHI_000410A 15.5 21.8 23.2 29.0 25.5 70.5
## 10 EHI_000430A 27.2 27.5 22.4 26.7 14.5 11.6## # A tibble: 6 × 7
## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EHI_000130A 45.3 108. 129. 180. 446. 516.
## 2 EHI_000140A 66.0 318. 257. 363. 352. 41.8
## 3 EHI_000240A 701. 1282. 877. 1209. 1993. 2290.
## 4 EHI_000250A 707. 430. 389. 334. 337. 711.
## 5 EHI_000260A 94.5 109. 35.2 112. 69.1 54.2
## 6 EHI_000280A 58.3 50.5 80.8 56.5 86.1 66.6Log-Normalización
## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 45.32378 107.82734 128.66351 180.07428 446.15729 516.14317
## 2 EHI_000140A 66.04322 317.74653 257.32703 363.20067 351.59134 41.84945
## 3 EHI_000240A 700.57610 1282.45711 877.45524 1208.63415 1993.15918 2290.09468
## 4 EHI_000250A 707.05093 430.16227 388.98271 334.20566 337.04273 710.66559
## 5 EHI_000260A 94.53245 108.97444 35.15805 112.16491 69.10588 54.24928
## 6 EHI_000280A 58.27343 50.47237 80.78872 56.46397 86.07926 66.64912
## log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1 7.500438 8.804639 9.014420 5.533681 6.765897
## 2 8.508590 8.461853 5.421205 6.067020 8.316266
## 3 10.240355 10.961565 11.161821 9.454456 10.325819
## 4 8.388903 8.401062 9.475056 9.467709 8.752087
## 5 6.822283 6.131464 5.787884 6.577919 6.781024
## 6 5.844586 6.444257 6.079999 5.889314 5.685726
## log2samplevsCDC53
## 1 7.018629
## 2 8.013055
## 3 9.778825
## 4 8.607266
## 5 5.176245
## 6 6.353830## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 45.32378 107.82734 128.66351 180.07428 446.15729 516.14317
## 2 EHI_000140A 66.04322 317.74653 257.32703 363.20067 351.59134 41.84945
## 3 EHI_000240A 700.57610 1282.45711 877.45524 1208.63415 1993.15918 2290.09468
## 4 EHI_000250A 707.05093 430.16227 388.98271 334.20566 337.04273 710.66559
## 5 EHI_000260A 94.53245 108.97444 35.15805 112.16491 69.10588 54.24928
## 6 EHI_000280A 58.27343 50.47237 80.78872 56.46397 86.07926 66.64912
## log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1 7.500438 8.804639 9.014420 5.533681 6.765897
## 2 8.508590 8.461853 5.421205 6.067020 8.316266
## 3 10.240355 10.961565 11.161821 9.454456 10.325819
## 4 8.388903 8.401062 9.475056 9.467709 8.752087
## 5 6.822283 6.131464 5.787884 6.577919 6.781024
## 6 5.844586 6.444257 6.079999 5.889314 5.685726
## log2samplevsCDC53
## 1 7.018629
## 2 8.013055
## 3 9.778825
## 4 8.607266
## 5 5.176245
## 6 6.353830Muestra 1
## [1] 6.460339## [1] 2.834041## [1] 7.500438 8.508590 10.240355 8.388903 6.822283## GenId CDC5_1 CDC5_2 CDC5_3
## Length:4691 Min. : 0.0 Min. : 0.00 Min. : 0.0
## Class :character 1st Qu.: 19.4 1st Qu.: 19.50 1st Qu.: 20.9
## Mode :character Median : 50.5 Median : 56.21 Median : 54.6
## Mean : 1930.9 Mean : 1790.28 Mean : 2024.1
## 3rd Qu.: 209.8 3rd Qu.: 248.92 3rd Qu.: 223.3
## Max. :405707.4 Max. :282737.06 Max. :384267.8
## pEhEx_1 pEhEx_2 pEhEx_3 log2sample1
## Min. : 0.00 Min. : 0.00 Min. : 0.0 Min. : 0.000
## 1st Qu.: 19.84 1st Qu.: 18.19 1st Qu.: 17.0 1st Qu.: 4.381
## Median : 55.70 Median : 56.98 Median : 60.4 Median : 5.825
## Mean : 1503.81 Mean : 1895.10 Mean : 2146.4 Mean : 6.460
## 3rd Qu.: 227.38 3rd Qu.: 250.96 3rd Qu.: 272.4 3rd Qu.: 7.835
## Max. :219640.26 Max. :288237.01 Max. :781911.9 Max. :17.745
## log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 4.262 1st Qu.: 4.174 1st Qu.: 4.352 1st Qu.: 4.358
## Median : 5.858 Median : 5.941 Median : 5.687 Median : 5.838
## Mean : 6.424 Mean : 6.345 Mean : 6.361 Mean : 6.456
## 3rd Qu.: 7.977 3rd Qu.: 8.095 3rd Qu.: 7.720 3rd Qu.: 7.965
## Max. :18.137 Max. :19.577 Max. :18.630 Max. :18.109
## log2samplevsCDC53
## Min. : 0.000
## 1st Qu.: 4.456
## Median : 5.797
## Mean : 6.531
## 3rd Qu.: 7.809
## Max. :18.552
## [1] 6.460339## [1] 2.834041## [1] 0.3670021 0.7227316 1.3337904 0.6804997 0.1277131 -0.2172704
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 3.118970e-17 0.01459893
## sd 9.998934e-01 0.01032296
## Loglikelihood: -6655.741 AIC: 13315.48 BIC: 13328.39
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Cálculo de cuantiles
## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8599158 0.8169091
## 70 -0.9371527 0.9617338
## 75 -0.9960331 1.1293972
## 80 -1.0282412 1.3839029
## 85 -1.0995019 1.7041875
## 90 -1.1392596 2.1899810
## 95 -1.2814439 2.6187913
## 99 -1.6734604 3.1869302
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra 2
## [1] 6.424318## [1] 3.078114## [1] 8.804639 8.461853 10.961565 8.401062 6.131464
Log-normalizacion
## [1] 6.424318## [1] 3.078114## [1] 0.773304903 0.661942766 1.474034750 0.642193202 -0.095140848
## [6] 0.006477743
Ajuste de modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -7.845328e-17 0.01459893
## sd 9.998934e-01 0.01032296
## Loglikelihood: -6655.741 AIC: 13315.48 BIC: 13328.39
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
Calculo de Cuantiles
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8804743 0.8477586
## 70 -0.9259047 0.9845324
## 75 -0.9762161 1.1416416
## 80 -1.0325848 1.3729846
## 85 -1.1709305 1.7147626
## 90 -1.2592113 2.1352442
## 95 -1.5101169 2.5267006
## 99 -2.0870956 3.0982665
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra 3
## [1] 6.345251## [1] 3.202711## [1] 9.014420 5.421205 11.161821 9.475056 5.787884
## [1] 6.345251## [1] 3.202711## [1] 0.83340927 -0.28851998 1.50390416 0.97723608 -0.17402974 -0.08282095
Ajustando Modelos
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 2.404120e-17 0.01459893
## sd 9.998934e-01 0.01032296
## Loglikelihood: -6655.741 AIC: 13315.48 BIC: 13328.39
## Correlation matrix:
## mean sd
## mean 1.000000e+00 1.713307e-11
## sd 1.713307e-11 1.000000e+00
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Calculo de Cuantiles
## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8675316 0.8402759
## 70 -0.9659157 0.9685687
## 75 -1.0459211 1.1104543
## 80 -1.1432722 1.3204414
## 85 -1.2676335 1.6079001
## 90 -1.4400018 1.9997962
## 95 -1.7227405 2.4584046
## 99 -1.9812123 3.0490511Creación de histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra pEhExvsCDC51
## [1] 6.360533## [1] 2.885557## [1] 5.533681 6.067020 9.454456 9.467709 6.577919
## [1] 6.360533## [1] 2.885557## [1] -0.28654857 -0.10171821 1.07220996 1.07680301 0.07533583 -0.16330282Primer histograma
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 4.727431e-17 0.01459893
## sd 9.998934e-01 0.01032296
## Loglikelihood: -6655.741 AIC: 13315.48 BIC: 13328.39
## Correlation matrix:
## mean sd
## mean 1.000000e+00 3.426614e-11
## sd 3.426614e-11 1.000000e+00
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Cálculo de cuantiles
## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8422310 0.8070408
## 70 -0.8866144 0.9258394
## 75 -0.9353250 1.1082535
## 80 -0.9892988 1.3411647
## 85 -1.0498123 1.6503084
## 90 -1.1186718 2.1232202
## 95 -1.2936703 2.7259168
## 99 -2.2042655 3.3471237Histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra pEhExvsCDC52
## [1] 6.456006## [1] 2.961412## [1] 6.765897 8.316266 10.325819 8.752087 6.781024Primer Histograma
## [1] 6.456006## [1] 2.961412## [1] 0.1046430 0.6281664 1.3067458 0.7753331 0.1097511 -0.2601055Segundo Histograma
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -1.572806e-17 0.01459893
## sd 9.998934e-01 0.01032296
## Loglikelihood: -6655.741 AIC: 13315.48 BIC: 13328.39
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
Cálculo de cuantiles
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8684632 0.8127421
## 70 -0.9078618 0.9668311
## 75 -0.9977360 1.1263553
## 80 -1.0497676 1.3726202
## 85 -1.1080283 1.6925494
## 90 -1.1742151 2.1332703
## 95 -1.3417825 2.5710200
## 99 -2.1800429 3.1603637Histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra pEhExvsCDC53
## [1] 6.53086## [1] 2.83016## [1] 7.018629 8.013055 9.778825 8.607266 5.176245Primer histograma
## [1] 6.53086## [1] 2.83016## [1] 0.17234655 0.52371392 1.14762562 0.73367078 -0.47863552 -0.06255138Segundo histograma
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 4.356074e-17 0.01459893
## sd 9.998934e-01 0.01032296
## Loglikelihood: -6655.741 AIC: 13315.48 BIC: 13328.39
## Correlation matrix:
## mean sd
## mean 1.000000e+00 -3.426614e-11
## sd -3.426614e-11 1.000000e+00
Calculo de cuantiles
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8498210 0.8013314
## 70 -0.8954944 0.9236834
## 75 -0.9456663 1.1144463
## 80 -1.0013208 1.3533967
## 85 -1.0316061 1.7113380
## 90 -1.0981755 2.2519051
## 95 -1.2178539 2.7028284
## 99 -1.5625210 3.3998336Histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Tercera Clasificacion: pEhExvsEhMyb10
## # A tibble: 10 × 7
## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EHI_000130A 69.9 61.2 502. 140. 346. 366.
## 2 EHI_000140A 216. 28.8 65.9 281. 272. 29.6
## 3 EHI_000240A 851. 489. 12365. 936. 1544. 1622.
## 4 EHI_000250A 413. 616. 1844. 259. 261. 503.
## 5 EHI_000260A 81.6 77.4 517. 86.9 53.5 38.4
## 6 EHI_000280A 35.9 48.6 59.6 43.7 66.7 47.2
## 7 EHI_000290A 12.6 23.4 47.0 14.8 11.3 62.0
## 8 EHI_000300A 104. 68.4 9.41 106. 92.0 24.2
## 9 EHI_000410A 17.0 10.8 144. 22.5 19.7 50.0
## 10 EHI_000430A 18.8 19.8 25.1 20.7 11.3 8.24## # A tibble: 6 × 7
## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EHI_000130A 69.9 61.2 502. 140. 346. 366.
## 2 EHI_000140A 216. 28.8 65.9 281. 272. 29.6
## 3 EHI_000240A 851. 489. 12365. 936. 1544. 1622.
## 4 EHI_000250A 413. 616. 1844. 259. 261. 503.
## 5 EHI_000260A 81.6 77.4 517. 86.9 53.5 38.4
## 6 EHI_000280A 35.9 48.6 59.6 43.7 66.7 47.2Log-Normalización
## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 69.92805 61.19384 501.73716 139.50869 345.55778 365.63668
## 2 EHI_000140A 216.05975 28.79710 65.85300 281.38193 272.31456 29.64622
## 3 EHI_000240A 850.79131 488.65084 12364.68511 936.36340 1543.74183 1622.30687
## 4 EHI_000250A 413.29272 616.43798 1843.88406 258.91867 261.04637 503.43668
## 5 EHI_000260A 81.58273 77.39221 517.41645 86.89736 53.52390 38.43028
## 6 EHI_000280A 35.86054 48.59511 59.58129 43.74425 66.67012 47.21435
## log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1 7.134516 8.436952 8.518207 6.148284 5.958700
## 2 8.141504 8.094418 4.937637 7.761948 4.897100
## 3 9.872465 10.593150 10.664720 9.734356 8.935610
## 4 8.021916 8.033678 8.978529 8.694507 9.270150
## 5 6.457748 5.768817 5.301232 6.367768 6.292638
## 6 5.483630 6.080447 5.591391 5.204005 5.632126
## log2samplevsCDC53
## 1 8.973661
## 2 6.062920
## 3 13.594055
## 4 10.849314
## 5 9.017968
## 6 5.920800## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 69.92805 61.19384 501.73716 139.50869 345.55778 365.63668
## 2 EHI_000140A 216.05975 28.79710 65.85300 281.38193 272.31456 29.64622
## 3 EHI_000240A 850.79131 488.65084 12364.68511 936.36340 1543.74183 1622.30687
## 4 EHI_000250A 413.29272 616.43798 1843.88406 258.91867 261.04637 503.43668
## 5 EHI_000260A 81.58273 77.39221 517.41645 86.89736 53.52390 38.43028
## 6 EHI_000280A 35.86054 48.59511 59.58129 43.74425 66.67012 47.21435
## log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1 7.134516 8.436952 8.518207 6.148284 5.958700
## 2 8.141504 8.094418 4.937637 7.761948 4.897100
## 3 9.872465 10.593150 10.664720 9.734356 8.935610
## 4 8.021916 8.033678 8.978529 8.694507 9.270150
## 5 6.457748 5.768817 5.301232 6.367768 6.292638
## 6 5.483630 6.080447 5.591391 5.204005 5.632126
## log2samplevsCDC53
## 1 8.973661
## 2 6.062920
## 3 13.594055
## 4 10.849314
## 5 9.017968
## 6 5.920800Muestra 1
## [1] 6.089174## [1] 2.844545## [1] 7.134516 8.141504 9.872465 8.021916 6.457748## GenId CDC5_1 CDC5_2 CDC5_3
## Length:4687 Min. : 0.00 Min. : 0.0 Min. : 0.0
## Class :character 1st Qu.: 14.34 1st Qu.: 16.2 1st Qu.: 12.5
## Mode :character Median : 41.24 Median : 41.4 Median : 50.2
## Mean : 1568.76 Mean : 1496.5 Mean : 4142.0
## 3rd Qu.: 189.61 3rd Qu.: 167.4 3rd Qu.: 239.9
## Max. :247688.75 Max. :404961.1 Max. :2325768.1
## pEhEx_1 pEhEx_2 pEhEx_3 log2sample1
## Min. : 0.00 Min. : 0.00 Min. : 0.0 Min. : 0.000
## 1st Qu.: 15.37 1st Qu.: 14.09 1st Qu.: 12.6 1st Qu.: 4.033
## Median : 43.15 Median : 44.13 Median : 43.9 Median : 5.464
## Mean : 1165.87 Mean : 1467.37 Mean : 1522.5 Mean : 6.089
## 3rd Qu.: 176.16 3rd Qu.: 194.38 3rd Qu.: 193.2 3rd Qu.: 7.469
## Max. :170161.59 Max. :223245.35 Max. :553907.7 Max. :17.377
## log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 3.915 1st Qu.: 3.768 1st Qu.: 3.940 1st Qu.: 4.104
## Median : 5.496 Median : 5.489 Median : 5.401 Median : 5.406
## Mean : 6.054 Mean : 5.945 Mean : 6.079 Mean : 6.086
## 3rd Qu.: 7.610 3rd Qu.: 7.602 3rd Qu.: 7.574 3rd Qu.: 7.396
## Max. :17.768 Max. :19.079 Max. :17.918 Max. :18.627
## log2samplevsCDC53
## Min. : 0.000
## 1st Qu.: 3.760
## Median : 5.677
## Mean : 6.068
## 3rd Qu.: 7.912
## Max. :21.149
## [1] 6.089174## [1] 2.844545## [1] 0.3674899 0.7214968 1.3300161 0.6794558 0.1295722 -0.2128788
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 3.587875e-17 0.01460516
## sd 9.998933e-01 0.01032736
## Loglikelihood: -6650.065 AIC: 13304.13 BIC: 13317.03
## Correlation matrix:
## mean sd
## mean 1.000000e+00 -3.429538e-11
## sd -3.429538e-11 1.000000e+00
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Cálculo de cuantiles
## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8467216 0.8152581
## 70 -0.9222155 0.9595311
## 75 -0.9796121 1.1276277
## 80 -1.0443431 1.3803954
## 85 -1.0800958 1.7000005
## 90 -1.1601851 2.1830409
## 95 -1.3725612 2.6106296
## 99 -1.7448623 3.1766074
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra 2
## [1] 6.054099## [1] 3.081702## [1] 8.436952 8.094418 10.593150 8.033678 5.768817
Log-normalizacion
## [1] 6.054099## [1] 3.081702## [1] 0.773226200 0.662075403 1.472903852 0.642365454 -0.092573117
## [6] 0.008549646
Ajuste de modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 1.403180e-16 0.01460516
## sd 9.998933e-01 0.01032736
## Loglikelihood: -6650.065 AIC: 13304.13 BIC: 13317.03
## Correlation matrix:
## mean sd
## mean 1.000000e+00 3.429538e-11
## sd 3.429538e-11 1.000000e+00
Calculo de cuantiles
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8686609 0.8470080
## 70 -0.9130056 0.9828038
## 75 -0.9619944 1.1403896
## 80 -1.0786897 1.3709267
## 85 -1.1501384 1.7083438
## 90 -1.2344905 2.1268598
## 95 -1.4696481 2.5248146
## 99 -1.9645312 3.0956436
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra 3
## [1] 5.944632## [1] 3.109086## [1] 8.518207 4.937637 10.664720 8.978529 5.301232
## [1] 5.944632## [1] 3.109086## [1] 0.8277593 -0.3238879 1.5181592 0.9758164 -0.2069419 -0.1136159
Ajustando Modelos
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -1.038159e-17 0.01460516
## sd 9.998933e-01 0.01032736
## Loglikelihood: -6650.065 AIC: 13304.13 BIC: 13317.03
## Correlation matrix:
## mean sd
## mean 1.000000e+00 1.714769e-11
## sd 1.714769e-11 1.000000e+00
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Calculo de cuantiles
## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8804873 0.835306
## 70 -0.9392233 0.967202
## 75 -1.0441669 1.113353
## 80 -1.1800004 1.329308
## 85 -1.2993075 1.626575
## 90 -1.3728657 2.029023
## 95 -1.5681818 2.501518
## 99 -1.9120192 3.109798Creación de histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra pEhExvsCDC51
## [1] 6.079067## [1] 3.007873## [1] 6.148284 7.761948 9.734356 8.694507 6.367768
## [1] 6.079067## [1] 3.007873## [1] 0.02301208 0.55949211 1.21524037 0.86953116 0.09598182 -0.29092372Primer histograma
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 3.734449e-17 0.01460516
## sd 9.998933e-01 0.01032736
## Loglikelihood: -6650.065 AIC: 13304.13 BIC: 13317.03
## Correlation matrix:
## mean sd
## mean 1.000000e+00 -3.429538e-11
## sd -3.429538e-11 1.000000e+00
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Calculo de cuantiles
## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8769970 0.8328201
## 70 -0.9183158 0.9594611
## 75 -0.9635325 1.1254243
## 80 -1.0134598 1.3759593
## 85 -1.0691946 1.6825955
## 90 -1.2049095 2.1281103
## 95 -1.3948811 2.6187546
## 99 -2.0210515 3.1616174Histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra pEhExvsCDC52
## [1] 6.08571## [1] 2.815509## [1] 5.958700 4.897100 8.935610 9.270150 6.292638Primer Histograma
## [1] 6.08571## [1] 2.815509## [1] -0.04511077 -0.42216506 1.01221485 1.13103560 0.07349607 -0.16110183Segundo Histograma
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 7.844575e-18 0.01460516
## sd 9.998933e-01 0.01032736
## Loglikelihood: -6650.065 AIC: 13304.13 BIC: 13317.03
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
Cálculo de cuantiles
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8241172 0.7977969
## 70 -0.8968636 0.9352333
## 75 -0.9375162 1.0847564
## 80 -0.9816741 1.3463833
## 85 -1.0299993 1.6629255
## 90 -1.0833612 2.1783677
## 95 -1.2880076 2.7298034
## 99 -2.1614954 3.4267131Histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra pEhExvsCDC53
## [1] 6.068265## [1] 3.43608## [1] 8.973661 6.062920 13.594055 10.849314 9.017968Primer histograma
## [1] 6.068265## [1] 3.43608## [1] 0.845555235 -0.001555479 2.190225383 1.391425525 0.858449916
## [6] -0.042916597Segundo histograma
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -1.107054e-16 0.01460516
## sd 9.998933e-01 0.01032736
## Loglikelihood: -6650.065 AIC: 13304.13 BIC: 13317.03
## Correlation matrix:
## mean sd
## mean 1.000000e+00 1.714769e-11
## sd 1.714769e-11 1.000000e+00
Calculo de cuantiles
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.7824916 0.8709604
## 70 -0.9330312 0.9884576
## 75 -1.1699608 1.1455636
## 80 -1.1699608 1.3353109
## 85 -1.1699608 1.5512574
## 90 -1.7660431 1.8682788
## 95 -1.7660431 2.3078117
## 99 -1.7660431 3.1171283Histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Cuarta Clasificacion: pEhExvsU2AF84
## # A tibble: 10 × 7
## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EHI_000130A 59.6 70.4 611. 140. 347. 382.
## 2 EHI_000140A 118. 207. 91.9 283. 273. 31.0
## 3 EHI_000240A 689. 871. 11090. 941. 1550. 1694.
## 4 EHI_000250A 426. 407. 1608. 260. 262. 526.
## 5 EHI_000260A 104. 110. 422. 87.3 53.7 40.1
## 6 EHI_000280A 40.4 35.2 108. 43.9 66.9 49.3
## 7 EHI_000290A 15.2 19.7 44.8 14.8 11.3 64.8
## 8 EHI_000300A 74.8 94.2 2.36 107. 92.4 25.2
## 9 EHI_000410A 15.2 15.5 134. 22.6 19.8 52.2
## 10 EHI_000430A 18.2 23.8 28.3 20.8 11.3 8.60## # A tibble: 6 × 7
## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EHI_000130A 59.6 70.4 611. 140. 347. 382.
## 2 EHI_000140A 118. 207. 91.9 283. 273. 31.0
## 3 EHI_000240A 689. 871. 11090. 941. 1550. 1694.
## 4 EHI_000250A 426. 407. 1608. 260. 262. 526.
## 5 EHI_000260A 104. 110. 422. 87.3 53.7 40.1
## 6 EHI_000280A 40.4 35.2 108. 43.9 66.9 49.3Log-Normalización
## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 59.61996 70.37838 610.61850 140.16143 346.92628 381.74940
## 2 EHI_000140A 118.22941 206.99523 91.94642 282.69848 273.39300 30.95265
## 3 EHI_000240A 689.16628 871.44992 11090.15226 940.74452 1549.85546 1693.79799
## 4 EHI_000250A 426.43427 406.74563 1607.88347 260.13011 262.08018 525.62192
## 5 EHI_000260A 104.08230 109.70747 422.01047 87.30394 53.73586 40.12381
## 6 EHI_000280A 40.42031 35.18919 108.44962 43.94892 66.93415 49.29497
## log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1 7.141202 8.442638 8.580256 5.921721 6.157415
## 2 8.148215 8.100100 4.997864 6.897596 7.700407
## 3 9.879192 10.598849 10.726898 9.430800 9.768929
## 4 8.028625 8.039359 9.040624 8.739559 8.671526
## 5 6.464406 5.774415 5.361902 6.715376 6.790609
## 6 5.490215 6.086065 5.652342 5.372266 5.177487
## log2samplevsCDC53
## 1 9.256488
## 2 6.538327
## 3 13.437122
## 4 10.651844
## 5 8.724550
## 6 6.774123## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 59.61996 70.37838 610.61850 140.16143 346.92628 381.74940
## 2 EHI_000140A 118.22941 206.99523 91.94642 282.69848 273.39300 30.95265
## 3 EHI_000240A 689.16628 871.44992 11090.15226 940.74452 1549.85546 1693.79799
## 4 EHI_000250A 426.43427 406.74563 1607.88347 260.13011 262.08018 525.62192
## 5 EHI_000260A 104.08230 109.70747 422.01047 87.30394 53.73586 40.12381
## 6 EHI_000280A 40.42031 35.18919 108.44962 43.94892 66.93415 49.29497
## log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1 7.141202 8.442638 8.580256 5.921721 6.157415
## 2 8.148215 8.100100 4.997864 6.897596 7.700407
## 3 9.879192 10.598849 10.726898 9.430800 9.768929
## 4 8.028625 8.039359 9.040624 8.739559 8.671526
## 5 6.464406 5.774415 5.361902 6.715376 6.790609
## 6 5.490215 6.086065 5.652342 5.372266 5.177487
## log2samplevsCDC53
## 1 9.256488
## 2 6.538327
## 3 13.437122
## 4 10.651844
## 5 8.724550
## 6 6.774123Muestra 1
## [1] 6.066239## [1] 2.837457## [1] 7.141202 8.148215 9.879192 8.028625 6.464406## GenId CDC5_1 CDC5_2 CDC5_3
## Length:4746 Min. : 0.00 Min. : 0.00 Min. : 0.0
## Class :character 1st Qu.: 15.16 1st Qu.: 14.49 1st Qu.: 14.1
## Mode :character Median : 41.43 Median : 42.43 Median : 49.5
## Mean : 1422.93 Mean : 1431.84 Mean : 3498.7
## 3rd Qu.: 168.75 3rd Qu.: 187.33 3rd Qu.: 237.5
## Max. :236529.55 Max. :222485.72 Max. :1942165.3
## pEhEx_1 pEhEx_2 pEhEx_3 log2sample1
## Min. : 0.00 Min. : 0.00 Min. : 0.0 Min. : 0.000
## 1st Qu.: 14.85 1st Qu.: 13.20 1st Qu.: 12.6 1st Qu.: 3.986
## Median : 42.17 Median : 42.42 Median : 44.7 Median : 5.432
## Mean : 1156.92 Mean : 1454.96 Mean : 1569.6 Mean : 6.066
## 3rd Qu.: 172.68 3rd Qu.: 191.38 3rd Qu.: 199.5 3rd Qu.: 7.440
## Max. :170957.75 Max. :224129.46 Max. :578317.1 Max. :17.383
## log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 3.828 1st Qu.: 3.767 1st Qu.: 4.014 1st Qu.: 3.953
## Median : 5.440 Median : 5.514 Median : 5.407 Median : 5.441
## Mean : 6.019 Mean : 5.939 Mean : 6.074 Mean : 6.080
## 3rd Qu.: 7.588 3rd Qu.: 7.647 3rd Qu.: 7.407 3rd Qu.: 7.557
## Max. :17.774 Max. :19.142 Max. :17.852 Max. :17.763
## log2samplevsCDC53
## Min. : 0.000
## 1st Qu.: 3.921
## Median : 5.658
## Mean : 6.152
## 3rd Qu.: 7.898
## Max. :20.889
## [1] 6.066239## [1] 2.837457## [1] 0.3788472 0.7337468 1.3437920 0.6916001 0.1403251 -0.2030074
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -5.346355e-17 0.01451411
## sd 9.998946e-01 0.01026298
## Loglikelihood: -6733.782 AIC: 13471.56 BIC: 13484.49
## Correlation matrix:
## mean sd
## mean 1.000000e+00 -3.386913e-11
## sd -3.386913e-11 1.000000e+00
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Cálculo de cuantiles
## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8625714 0.8124762
## 70 -0.9422460 0.9640896
## 75 -0.9718431 1.1342860
## 80 -1.0367684 1.3790909
## 85 -1.0726303 1.7033941
## 90 -1.1529698 2.1934547
## 95 -1.3038487 2.6233257
## 99 -1.7398510 3.1888833
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra 2
## [1] 6.018835## [1] 3.085311## [1] 8.442638 8.100100 10.598849 8.039359 5.774415
Log-normalizacion
## [1] 6.018835## [1] 3.085311## [1] 0.78559421 0.67457202 1.48445747 0.65488484 -0.07922075 0.02179028
Ajuste de modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 4.519200e-17 0.01451411
## sd 9.998946e-01 0.01026298
## Loglikelihood: -6733.782 AIC: 13471.56 BIC: 13484.49
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
Calculo de cuantiles
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8988548 0.8463966
## 70 -0.9478079 0.9891035
## 75 -1.0024917 1.1467315
## 80 -1.0644280 1.3749391
## 85 -1.1358390 1.6937524
## 90 -1.2201564 2.1241067
## 95 -1.4552922 2.5236980
## 99 -1.9508033 3.0984441
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra 3
## [1] 5.938851## [1] 3.137257## [1] 8.580256 4.997864 10.726898 9.040624 5.361902
## [1] 5.938851## [1] 3.137257## [1] 0.84194733 -0.29993956 1.52618884 0.98868937 -0.18390246 -0.09132474
Ajustando Modelos
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 1.897633e-17 0.01451411
## sd 9.998946e-01 0.01026298
## Loglikelihood: -6733.782 AIC: 13471.56 BIC: 13484.49
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Calculo de cuantiles
## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8813312 0.8377086
## 70 -0.9785407 0.9709584
## 75 -1.0570406 1.1167404
## 80 -1.1517563 1.3292508
## 85 -1.2711789 1.6268787
## 90 -1.4329274 2.0299674
## 95 -1.5417724 2.4960043
## 99 -1.8930077 3.1029256Creación de histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra pEhExvsCDC51
## [1] 6.073889## [1] 2.856279## [1] 5.921721 6.897596 9.430800 8.739559 6.715376
## [1] 6.073889## [1] 2.856279## [1] -0.05327506 0.28838461 1.17527408 0.93326643 0.22458816 -0.24564228Primer histograma
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -1.363897e-18 0.01451411
## sd 9.998946e-01 0.01026298
## Loglikelihood: -6733.782 AIC: 13471.56 BIC: 13484.49
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Calculo de cuantiles
## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8665460 0.8005323
## 70 -0.9105349 0.9400738
## 75 -0.9587235 1.1113009
## 80 -1.0119991 1.3461767
## 85 -1.0715638 1.6701383
## 90 -1.0715638 2.1555034
## 95 -1.3093554 2.7085444
## 99 -2.1265041 3.2907409Histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra pEhExvsCDC52
## [1] 6.079551## [1] 2.958446## [1] 6.157415 7.700407 9.768929 8.671526 6.790609Primer Histograma
## [1] 6.079551## [1] 2.958446## [1] 0.02631943 0.54787417 1.24706626 0.87612726 0.24034856 -0.30491137Segundo Histograma
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -9.044811e-17 0.01451411
## sd 9.998946e-01 0.01026298
## Loglikelihood: -6733.782 AIC: 13471.56 BIC: 13484.49
## Correlation matrix:
## mean sd
## mean 1.000000e+00 -3.386913e-11
## sd -3.386913e-11 1.000000e+00
Cálculo de cuantiles
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8703761 0.8136491
## 70 -0.9170047 0.9590243
## 75 -0.9685679 1.1169792
## 80 -1.0262349 1.3717093
## 85 -1.0916475 1.6775570
## 90 -1.1672140 2.1543218
## 95 -1.3663234 2.6103392
## 99 -2.0549813 3.1594798Histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra pEhExvsCDC53
## [1] 6.151641## [1] 3.315575## [1] 9.256488 6.538327 13.437122 10.651844 8.724550Primer histograma
## [1] 6.151641## [1] 3.315575## [1] 0.9364429 0.1166271 2.1973504 1.3572917 0.7760066 0.1877447Segundo histograma
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -1.080389e-16 0.01451411
## sd 9.998946e-01 0.01026298
## Loglikelihood: -6733.782 AIC: 13471.56 BIC: 13484.49
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
Calculo de cuantiles
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8351247 0.8675257
## 70 -0.9466147 0.9977469
## 75 -0.9466147 1.1516663
## 80 -1.0968948 1.3476203
## 85 -1.3283399 1.5933747
## 90 -1.3283399 1.8884142
## 95 -1.8553769 2.3619041
## 99 -1.8553769 3.1601836Histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Quinta Clasificacion: pEhExvsUmasM
## # A tibble: 10 × 7
## GenId UmasM_1 UmasM_2 UmasM_3 pEhEx_1 pEhEx_2 pEhEx_3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EHI_000130A 52.8 428. 877. 175. 432. 478.
## 2 EHI_000140A 118. 371. 43.7 353. 341. 38.8
## 3 EHI_000240A 744. 1165. 7104. 1175. 1932. 2122.
## 4 EHI_000250A 704. 213. 1578. 325. 327. 659.
## 5 EHI_000260A 119. 96.2 139. 109. 67.0 50.3
## 6 EHI_000280A 57.6 59.6 34.3 54.9 83.4 61.8
## 7 EHI_000290A 26.4 19.7 70.2 18.5 14.1 81.2
## 8 EHI_000300A 80.4 115. 15.6 134. 115. 31.6
## 9 EHI_000410A 20.4 24.4 139. 28.2 24.7 65.4
## 10 EHI_000430A 31.2 35.9 14.0 26.0 14.1 10.8## # A tibble: 6 × 7
## GenId UmasM_1 UmasM_2 UmasM_3 pEhEx_1 pEhEx_2 pEhEx_3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EHI_000130A 52.8 428. 877. 175. 432. 478.
## 2 EHI_000140A 118. 371. 43.7 353. 341. 38.8
## 3 EHI_000240A 744. 1165. 7104. 1175. 1932. 2122.
## 4 EHI_000250A 704. 213. 1578. 325. 327. 659.
## 5 EHI_000260A 119. 96.2 139. 109. 67.0 50.3
## 6 EHI_000280A 57.6 59.6 34.3 54.9 83.4 61.8Log-Normalización
## GenId UmasM_1 UmasM_2 UmasM_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 52.79569 428.31302 877.32975 175.05490 432.49403 478.29632
## 2 EHI_000140A 117.59041 370.70763 43.71038 353.07682 340.82410 38.78078
## 3 EHI_000240A 743.93931 1164.98430 7104.49766 1174.94473 1932.12006 2122.17063
## 4 EHI_000250A 704.34254 212.80109 1578.25690 324.89002 326.72103 658.55515
## 5 EHI_000260A 118.79031 96.23489 138.93656 109.03843 66.98956 50.27139
## 6 EHI_000280A 57.59530 59.63852 34.34387 54.89009 83.44314 61.76199
## log2sample1 log2sample2 log2sample3 log2samplevsumasM1 log2samplevsumasM2
## 1 7.459882 8.759868 8.904774 5.749419 8.745886
## 2 8.467919 8.417110 5.314000 6.889844 8.538024
## 3 10.199605 10.916716 11.052005 9.540979 10.187333
## 4 8.348241 8.356324 9.365349 9.462180 7.740125
## 5 6.781864 6.087241 5.680082 6.904367 6.603402
## 6 5.804521 6.399908 5.971819 5.872713 5.922163
## log2samplevsumasM3
## 1 9.778619
## 2 5.482538
## 3 12.794720
## 4 10.625030
## 5 7.128629
## 6 5.143388## GenId UmasM_1 UmasM_2 UmasM_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 52.79569 428.31302 877.32975 175.05490 432.49403 478.29632
## 2 EHI_000140A 117.59041 370.70763 43.71038 353.07682 340.82410 38.78078
## 3 EHI_000240A 743.93931 1164.98430 7104.49766 1174.94473 1932.12006 2122.17063
## 4 EHI_000250A 704.34254 212.80109 1578.25690 324.89002 326.72103 658.55515
## 5 EHI_000260A 118.79031 96.23489 138.93656 109.03843 66.98956 50.27139
## 6 EHI_000280A 57.59530 59.63852 34.34387 54.89009 83.44314 61.76199
## log2sample1 log2sample2 log2sample3 log2samplevsumasM1 log2samplevsumasM2
## 1 7.459882 8.759868 8.904774 5.749419 8.745886
## 2 8.467919 8.417110 5.314000 6.889844 8.538024
## 3 10.199605 10.916716 11.052005 9.540979 10.187333
## 4 8.348241 8.356324 9.365349 9.462180 7.740125
## 5 6.781864 6.087241 5.680082 6.904367 6.603402
## 6 5.804521 6.399908 5.971819 5.872713 5.922163
## log2samplevsumasM3
## 1 9.778619
## 2 5.482538
## 3 12.794720
## 4 10.625030
## 5 7.128629
## 6 5.143388Muestra 1
## [1] 6.24652## [1] 2.884429## [1] 7.459882 8.467919 10.199605 8.348241 6.781864## GenId UmasM_1 UmasM_2 UmasM_3
## Length:4919 Min. : 0.0 Min. : 0.00 Min. : 0.0
## Class :character 1st Qu.: 16.8 1st Qu.: 18.30 1st Qu.: 14.0
## Mode :character Median : 46.8 Median : 52.18 Median : 54.6
## Mean : 1915.1 Mean : 1003.67 Mean : 3444.8
## 3rd Qu.: 198.0 3rd Qu.: 193.15 3rd Qu.: 295.0
## Max. :340994.2 Max. :145896.83 Max. :1488475.8
## pEhEx_1 pEhEx_2 pEhEx_3 log2sample1
## Min. : 0.00 Min. : 0.00 Min. : 0.0 Min. : 0.000
## 1st Qu.: 16.32 1st Qu.: 15.28 1st Qu.: 15.1 1st Qu.: 4.114
## Median : 48.96 Median : 48.19 Median : 52.4 Median : 5.643
## Mean : 1394.44 Mean : 1752.18 Mean : 1898.2 Mean : 6.247
## 3rd Qu.: 199.53 3rd Qu.: 222.71 3rd Qu.: 231.2 3rd Qu.: 7.648
## Max. :213518.02 Max. :279409.95 Max. :724577.3 Max. :17.704
## log2sample2 log2sample3 log2samplevsumasM1 log2samplevsumasM2
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 4.025 1st Qu.: 4.007 1st Qu.: 4.154 1st Qu.: 4.270
## Median : 5.620 Median : 5.739 Median : 5.579 Median : 5.733
## Mean : 6.187 Mean : 6.152 Mean : 6.222 Mean : 6.256
## 3rd Qu.: 7.805 3rd Qu.: 7.860 3rd Qu.: 7.637 3rd Qu.: 7.601
## Max. :18.092 Max. :19.467 Max. :18.379 Max. :17.155
## log2samplevsumasM3
## Min. : 0.000
## 1st Qu.: 3.912
## Median : 5.798
## Mean : 6.317
## 3rd Qu.: 8.210
## Max. :20.505
## [1] 6.24652## [1] 2.884429## [1] 0.4206592 0.7701346 1.3704911 0.7286438 0.1855978 -0.1532363
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -1.310539e-16 0.01425665
## sd 9.998983e-01 0.01008093
## Loglikelihood: -6979.259 AIC: 13962.52 BIC: 13975.52
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Cálculo de cuantiles
## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8877976 0.8234548
## 70 -0.9174931 0.9668385
## 75 -0.9827617 1.1346553
## 80 -1.0188953 1.3615138
## 85 -1.1000839 1.6923518
## 90 -1.1462228 2.1593960
## 95 -1.3906263 2.6069712
## 99 -2.1655999 3.1547856
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra 2
## [1] 6.18657## [1] 3.14197## [1] 8.759868 8.417110 10.916716 8.356324 6.087241
Log-normalizacion
## [1] 6.18657## [1] 3.14197## [1] 0.81900802 0.70991786 1.50547137 0.69057145 -0.03161338 0.06789964
Ajuste de modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -1.093861e-16 0.01425665
## sd 9.998983e-01 0.01008093
## Loglikelihood: -6979.259 AIC: 13962.52 BIC: 13975.52
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
Calculo de cuantiles
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8936351 0.8467827
## 70 -0.8936351 0.9884290
## 75 -1.0112465 1.1344069
## 80 -1.0836965 1.3626690
## 85 -1.1697595 1.6777371
## 90 -1.2757625 2.1128235
## 95 -1.6121684 2.5115181
## 99 -1.9690098 3.0656774
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra 3
## [1] 6.152478## [1] 3.158081## [1] 8.904774 5.314000 11.052005 9.365349 5.680082
## [1] 6.152478## [1] 3.158081## [1] 0.87150918 -0.26550237 1.55142547 1.01734948 -0.14958317 -0.05720518
Ajustando Modelos
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 1.292160e-16 0.01425665
## sd 9.998983e-01 0.01008093
## Loglikelihood: -6979.259 AIC: 13962.52 BIC: 13975.52
## Correlation matrix:
## mean sd
## mean 1.00000e+00 -3.26782e-11
## sd -3.26782e-11 1.00000e+00
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Calculo de cuantiles
## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8811857 0.8359346
## 70 -0.9495345 0.9646852
## 75 -1.0299396 1.1073525
## 80 -1.1855441 1.3225658
## 85 -1.2519401 1.6081997
## 90 -1.4233556 2.0263238
## 95 -1.7009100 2.4893700
## 99 -1.9481699 3.1039165Creación de histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra pEhExvsumasM1
## [1] 6.221901## [1] 2.955459## [1] 5.749419 6.889844 9.540979 9.462180 6.904367
## [1] 6.221901## [1] 2.955459## [1] -0.1598676 0.2260030 1.1230332 1.0963710 0.2309173 -0.1181501Primer histograma
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -9.853035e-17 0.01425665
## sd 9.998983e-01 0.01008093
## Loglikelihood: -6979.259 AIC: 13962.52 BIC: 13975.52
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Calculo de cuantiles
## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8531906 0.8236011
## 70 -0.9004672 0.9332295
## 75 -0.9528182 1.1118993
## 80 -0.9528182 1.3280269
## 85 -1.0781339 1.6545578
## 90 -1.1553694 2.1045904
## 95 -1.3603164 2.6974724
## 99 -2.1052233 3.3025359Histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra pEhExvsumasM2
## [1] 6.255602## [1] 2.72471## [1] 8.745886 8.538024 10.187333 7.740125 6.603402Primer Histograma
## [1] 6.255602## [1] 2.72471## [1] 0.9139630 0.8376754 1.4429905 0.5448372 0.1276466 -0.1223761Segundo Histograma
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 1.626526e-16 0.01425665
## sd 9.998983e-01 0.01008093
## Loglikelihood: -6979.259 AIC: 13962.52 BIC: 13975.52
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
Cálculo de cuantiles
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8779788 0.8200071
## 70 -0.9297388 0.9699103
## 75 -0.9871123 1.1247423
## 80 -1.0514667 1.3424300
## 85 -1.1247392 1.7188713
## 90 -1.2098054 2.1841922
## 95 -1.3112040 2.5940822
## 99 -1.8422575 3.1504680Histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
Muestra pEhExvsumasM3
## [1] 6.317092## [1] 3.410882## [1] 9.778619 5.482538 12.794720 10.625030 7.128629** Primer histograma**
## [1] 6.317092## [1] 3.410882## [1] 1.0148481 -0.2446740 1.8991065 1.2629984 0.2379259 -0.3441057Segundo histograma
Ajustando Modelo
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -2.967667e-17 0.01425665
## sd 9.998983e-01 0.01008093
## Loglikelihood: -6979.259 AIC: 13962.52 BIC: 13975.52
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
Calculo de cuantiles
## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293## LimInf LimSup
## 65 -0.8628952 0.8884901
## 70 -0.9319289 1.0207444
## 75 -1.0144701 1.1904247
## 80 -1.1171230 1.3814634
## 85 -1.2529569 1.6102025
## 90 -1.4542681 1.9460444
## 95 -1.8520407 2.4110529
## 99 -1.8520407 3.0342081Histogramas
Grafica Cuantiles del \(65\%\) y \(80\%\)
Grafica Cuantiles del \(70\%\) y \(85\%\)
